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Distinct and Shared Neural Basis Underlying Music and Language:A Perspective from Meta-analysis

2014· article· en· W3147797909 sur OpenAlexaboutno aff
Lai Ha

Notice bibliographique

RevueActa Psychologica Sinica · 2014
Typearticle
Langueen
DomaineNeuroscience
ThématiqueNeuroscience and Music Perception
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésNeural substrateFunctional magnetic resonance imagingSentence processingPsychologySentenceDissociation (chemistry)Music psychologyComputer scienceCognitive psychologySpeech recognitionNatural language processingCognitionNeuroscienceMusicologyChemistry
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Music and language are arguably the most characteristic traits of human beings. On one hand, previous functional magnetic resonance imaging(fMRI) studies have identified multiple cortical regions that are involved in the processing of both music and language, suggesting shared neural substrate for music and language. On the other hand, neuropsychological studies on brain-lesioned patients show the double dissociation between music and language, suggesting distinct neural substrates for music and language respectively. Here we used meta-analysis to examine the relation of the neural basis underlying music and language. First, we adopted the data from three meta-analysis studies on music and language respectively. Specifically, for the neural substrate of music, we focused on two processing levels specific to music processing, which were interval analysis(15 contrasts and 63 peaks) and structure analysis(19 contrasts and 217 peaks)(Lai, Xu, Song, Liu, 2013). For the neural substrate of language, three processing levels specific to language processing were selected, which were phonological analysis(86 contrasts and 344 peaks), lexico-semantic analysis(111contrasts and 339 peaks), and sentence analysis(65 contrasts and 218 peaks)(Vigneau et al., 2006; Vigneau et al., 2011). Second, we projected these peak activation elicited by processing either music or language onto the MNI(Montreal Neurological Institute) space to visualize the distribution of cortical regions involved in music and language with Caret. Finally, to explore the relation of neural substrates underlying music and language, we calculated whether pairs of peak activation were spatially overlapped or dissociated by K-means clustering and multivariate analysis of variance(MANOVA). The overlapping rate was estimated to quantify the extent to which music and language shared common neural substrates. We finally found 11 pairs of overlapping clusters. Music and language shared neural substrates at all levels of processing tested. Specially, the overlapped clusters from phonological processing of language and music perception were mainly in the auditory-motor loop, the overlapped clusters from semantic processing and music perception were in core loop, and the overlapped clusters of sentence processing and music were in cognition-emotion loop. In addition, the neural substrate underlying interval analysis of music overlapped with that underlying language processing as much as 50% in left hemisphere, which was mainly in the left superior temporal gyrus, left precentral gyrus, left pars triangularis of the inferior frontal gyrus and right insular. The neural substrate underlying structure analysis of music overlapped with that underlying language processing as much as 7% and 14% in the left and right hemisphere respectively, which was mainly in left Rolandic operculum, right pars opercularis of the inferior frontal gyrus and right insular. In sum, our study illuminates the functionality of the distinct and shared neural substrates underlying music and language. That is, for lower-level processes, such as interval analysis, phonological analysis and lexico-semantic analysis, music and language are more likely to share the same neural substrate in auditory analysis, auditory-motor integration and working memory. By contrast, for higher-level processes, especially in the structure analysis and sentence analysis, the neural substrate underlying music is more likely distinct from that underlying language. Models were proposed to illustrate the distinct and shared neural mechanisms underlying music and language, which invites future studies on the relation between music and language.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,985
Score d'incertitude au seuil0,960

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,251
Tête enseignante GPT0,391
Écart entre enseignants0,139 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2014
Routes d'admission1
Résumé présentoui

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